Contrast maps for Cue effects (high_cue > low_cue)

Model (6cond with high/low cue & rampup/rampdown)
parameter:
Table of Contents

Pain > (Vicarious & Cognitive)

High Cue > Low Cue

Pain > VC :: load dataset

clear all;
close all;
 
contrast_of_interest = 'P_VC_CUE_cue_high_gt_low'
contrast_of_interest = 'P_VC_CUE_cue_high_gt_low'
 
 
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
contrast_name = {
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'motor',... %motor
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
 
};
 
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
spm('Defaults','fMRI')
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead. loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 28753056 bytes Loading image number: 72 Elapsed time is 2.402374 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 6896897 Bit rate: 22.72 bits

Pain > VC :: check data coverage

m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans'); % display
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:50:02 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions

Pain > VC :: Plot diagnostics, before l2norm

drawnow; snapnow
 
[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);
______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 20.83% Expected 3.60 outside 95% ellipsoid, found 5 Potential outliers based on mahalanobis distance: Bonferroni corrected: 2 images Cases 36 44 Uncorrected: 5 images Cases 36 37 44 48 68 Retained 11 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 54.17% Expected 3.60 outside 95% ellipsoid, found 0 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 0 images Cases Mahalanobis (cov and corr, q<0.05 corrected): 2 images Outlier_count Percentage _____________ __________ global_mean 2 2.7778 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 3 4.1667 mahal_cov_uncor 5 6.9444 mahal_cov_corrected 2 2.7778 mahal_corr_uncor 0 0 mahal_corr_corrected 0 0 Overall_uncorrected 5 6.9444 Overall_corrected 3 4.1667
Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:50:26 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions

Pain > VC :: run robfit

set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;
 

Pain > VC :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
current length is 72
%for s = 1:length(wh_outlier_corr)
%disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 3 participants, size is now 69
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0082" "participants that are outliers:... sub-0093" "participants that are outliers:... sub-0098"
disp(n);
{'sub-0082'} {'sub-0093'} {'sub-0098'}

Pain > VC :: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Pain > VC :: ttest

t = ttest(imgs2);
One-sample t-test Calculating t-statistics and p-values
orthviews(t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:50:30 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
fdr_t = threshold(t, .05, 'fdr');
Image 1 FDR q < 0.050 threshold is 0.002851 Image 1 112 contig. clusters, sizes 1 to 2520 Positive effect: 5021 voxels, min p-value: 0.00000000 Negative effect: 671 voxels, min p-value: 0.00000000
orthviews(fdr_t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:50:31 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
fdr_t = threshold(t, .001, 'fdr');
Image 1 FDR q < 0.001 threshold is 0.000005 Image 1 9 contig. clusters, sizes 1 to 260 Positive effect: 277 voxels, min p-value: 0.00000000 Negative effect: 271 voxels, min p-value: 0.00000000
orthviews(fdr_t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:50:32 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
create_figure('montage'); axis off;
montage(fdr_t);
Setting up fmridisplay objects Compressed NIfTI files are not supported. sagittal montage: 0 voxels displayed, 548 not displayed on these slices sagittal montage: 0 voxels displayed, 548 not displayed on these slices sagittal montage: 0 voxels displayed, 548 not displayed on these slices axial montage: 115 voxels displayed, 433 not displayed on these slices axial montage: 131 voxels displayed, 417 not displayed on these slices
drawnow, snapnow;

Pain > VC :: Neurosynth similarity

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ _______________ __________ ______________ -0.097326 {'selection' } 0.11814 {'body' } -0.094948 {'time' } 0.11486 {'gestures' } -0.086903 {'age' } 0.11158 {'gesture' } -0.085876 {'development'} 0.10731 {'perception'} -0.084058 {'english' } 0.10676 {'nogo' } -0.077025 {'decision' } 0.103 {'biological'} -0.076842 {'languages' } 0.1027 {'masked' } -0.070151 {'chinese' } 0.096733 {'salient' } -0.069668 {'children' } 0.09438 {'sexual' } -0.06946 {'distractor' } 0.090781 {'actions' }
% [image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( m, 'images_are_replicates', false, 'noverbose');

Pain > VC :: Pattern Phil
[obj, names] = load_image_set('pain_cog_emo');
Loaded images: /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
 
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain 0.0078 1.3362 0.1858 0.0000 Cog Wholebrain -0.0147 -3.5282 0.0007 1.0000 *** Emo Wholebrain 0.0058 1.0528 0.2960 0.0000
axis image
 
subplot(1, 3, 2)
 
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} 0.0078077 0.0058408 1.3368 0.18557 0.15754 {'Cog Wholebrain' } -0.01473 0.0041711 -3.5315 0.00073055 -0.41619 {'Emo Wholebrain' } 0.0058031 0.0055186 1.0515 0.29657 0.12393
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [19.0001 20.0001 21.0001]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
 
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
 
clear csim
for i = 1:3
 
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
 
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
 
subplot(1, 3, 3)
 
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ _________ ________ {'Pain Wholebrain'} 0.0074586 0.0057752 1.2915 0.20072 0.1522 {'Cog Wholebrain' } -0.013329 0.003956 -3.3692 0.0012216 -0.39706 {'Emo Wholebrain' } 0.0051335 0.0056034 0.91613 0.3627 0.10797
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [22.0001 23.0001 24.0001]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')

Vicarious > (Pain & Cognitive)

High Cue > Low Cue

clear all;
close all;

Vicarious > PC :: load dataset

clear all;
close all;
 
contrast_of_interest = 'V_PC_CUE_cue_high_gt_low'
contrast_of_interest = 'V_PC_CUE_cue_high_gt_low'
 
 
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
contrast_name = {
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'motor',... %motor
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
 
};
 
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
spm('Defaults','fMRI')
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead. loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 28753056 bytes Loading image number: 72 Elapsed time is 2.608826 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 6900404 Bit rate: 22.72 bits

Vicarious > PC :: check data coverage

m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans') % display
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:51:08 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions
ans = 1×1 cell array
{1×1 region}

Vicarious > PC :: Plot diagnostics, before l2norm

drawnow; snapnow;
 
[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);
______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 3 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 25.00% Expected 3.60 outside 95% ellipsoid, found 9 Potential outliers based on mahalanobis distance: Bonferroni corrected: 3 images Cases 8 34 39 Uncorrected: 9 images Cases 8 16 18 34 36 39 48 65 68 Retained 16 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 52.78% Expected 3.60 outside 95% ellipsoid, found 1 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 1 images Cases 5 Mahalanobis (cov and corr, q<0.05 corrected): 3 images Outlier_count Percentage _____________ __________ global_mean 4 5.5556 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 4 5.5556 mahal_cov_uncor 9 12.5 mahal_cov_corrected 3 4.1667 mahal_corr_uncor 1 1.3889 mahal_corr_corrected 0 0 Overall_uncorrected 10 13.889 Overall_corrected 5 6.9444
Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:51:35 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions

Vicarious > PC :: run robfit

set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;

Vicarious > PC :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
current length is 72
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 5 participants, size is now 67
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... su…" "participants that are outliers:... su…" "participants that are outliers:... su…" "participants that are outliers:... su…" "participants that are outliers:... su…"
disp(n);
{'sub-0035'} {'sub-0080'} {'sub-0086'} {'sub-0098'} {'sub-0123'}

Vicarious > PC :: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Vicarious > PC :: ttest

t = ttest(imgs2);
One-sample t-test Calculating t-statistics and p-values
orthviews(t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:51:39 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
fdr_t = threshold(t, .05, 'fdr');
Image 1 FDR q < 0.050 threshold is 0.000201 Image 1 12 contig. clusters, sizes 1 to 161 Positive effect: 308 voxels, min p-value: 0.00000000 Negative effect: 94 voxels, min p-value: 0.00001121
orthviews(fdr_t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:51:40 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
create_figure('montage'); axis off;
montage(fdr_t);
Setting up fmridisplay objects Compressed NIfTI files are not supported. sagittal montage: 0 voxels displayed, 402 not displayed on these slices sagittal montage: 0 voxels displayed, 402 not displayed on these slices sagittal montage: 0 voxels displayed, 402 not displayed on these slices axial montage: 85 voxels displayed, 317 not displayed on these slices axial montage: 87 voxels displayed, 315 not displayed on these slices
drawnow, snapnow;

Vicarious > PC :: Neurosynth similarity

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ ______________ __________ _______________ -0.13549 {'actions' } 0.090964 {'dopamine' } -0.10609 {'action' } 0.086193 {'age' } -0.10014 {'gesture' } 0.08542 {'depression' } -0.09958 {'gestures' } 0.082329 {'decision' } -0.093939 {'nogo' } 0.080135 {'probability'} -0.093746 {'body' } 0.079719 {'outcome' } -0.09148 {'rest' } 0.079306 {'languages' } -0.088971 {'videos' } 0.07402 {'monetary' } -0.081234 {'biological'} 0.073896 {'reward' } -0.078146 {'mirror' } 0.073883 {'selection' }

Vicarious > PC :: Pattern Phil
[obj, names] = load_image_set('pain_cog_emo');
Loaded images: /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
 
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.0047 -0.6872 0.4942 0.0000 Cog Wholebrain 0.0065 1.9184 0.0591 0.0000 Emo Wholebrain -0.0014 -0.2286 0.8198 0.0000
axis image
 
subplot(1, 3, 2)
 
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ ________ ________ _________ {'Pain Wholebrain'} -0.0046898 0.0068556 -0.68409 0.49615 -0.08062 {'Cog Wholebrain' } 0.0065347 0.0034067 1.9182 0.059107 0.22606 {'Emo Wholebrain' } -0.0014139 0.0061671 -0.22926 0.81933 -0.027018
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [19.0002 20.0002 21.0002]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
 
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
 
clear csim
for i = 1:3
 
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
 
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
 
subplot(1, 3, 3)
 
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ ________ ________ _________ {'Pain Wholebrain'} -0.0059372 0.0066877 -0.88779 0.37765 -0.10463 {'Cog Wholebrain' } 0.0056382 0.0032126 1.755 0.083569 0.20683 {'Emo Wholebrain' } 0.00049394 0.0059324 0.083262 0.93388 0.0098125
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [22.0002 23.0002 24.0002]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')

Cognitive > (Pain & Vicarious)

High cue > Low cue

Cognitive > PV :: load dataset

clear all;
close all;
 
contrast_of_interest = 'C_PV_CUE_cue_high_gt_low'
contrast_of_interest = 'C_PV_CUE_cue_high_gt_low'
 
 
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
contrast_name = {
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'motor',... %motor
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
 
};
 
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
spm('Defaults','fMRI')
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead. loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 28753056 bytes Loading image number: 72 Elapsed time is 2.490657 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 6901443 Bit rate: 22.72 bits

Cognitive > PV :: check data coverage

m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans'); % display
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:52:15 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions

Cognitive > PV :: Plot diagnostics, before l2norm

drawnow; snapnow;
 
[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);
______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 5 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 22.22% Expected 3.60 outside 95% ellipsoid, found 7 Potential outliers based on mahalanobis distance: Bonferroni corrected: 3 images Cases 36 39 44 Uncorrected: 7 images Cases 18 34 36 37 39 44 65 Retained 10 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 56.94% Expected 3.60 outside 95% ellipsoid, found 0 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 0 images Cases Mahalanobis (cov and corr, q<0.05 corrected): 3 images Outlier_count Percentage _____________ __________ global_mean 4 5.5556 global_mean_to_variance 2 2.7778 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 3 4.1667 mahal_cov_uncor 7 9.7222 mahal_cov_corrected 3 4.1667 mahal_corr_uncor 0 0 mahal_corr_corrected 0 0 Overall_uncorrected 7 9.7222 Overall_corrected 4 5.5556
Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:52:42 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions

Cognitive > PV :: run robfit

set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;

Cognitive > PV :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
current length is 72
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 4 participants, size is now 68
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0082" "participants that are outliers:... sub-0084" "participants that are outliers:... sub-0086" "participants that are outliers:... sub-0093"
disp(n);
{'sub-0082'} {'sub-0084'} {'sub-0086'} {'sub-0093'}

Cognitive > PV:: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Cognitive > PV :: ttest

t = ttest(imgs2);
One-sample t-test Calculating t-statistics and p-values
orthviews(t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:52:45 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
fdr_t = threshold(t, .05, 'fdr');
Image 1 FDR q < 0.050 threshold is 0.000000 Image 1 0 contig. clusters, sizes to Positive effect: 0 voxels, min p-value: 0.00007772 Negative effect: 0 voxels, min p-value: 0.00003052
orthviews(fdr_t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:52:46 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
create_figure('montage'); axis off;
montage(fdr_t);
Setting up fmridisplay objects Compressed NIfTI files are not supported.
drawnow, snapnow;
 
 

Cognitive > PV :: Neurosynth similarity

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ _______________ __________ _____________ -0.14379 {'pain' } 0.13256 {'visual' } -0.12341 {'rating' } 0.11599 {'eye' } -0.12171 {'painful' } 0.11384 {'spatial' } -0.11981 {'ratings' } 0.099954 {'shape' } -0.10972 {'noxious' } 0.097815 {'time' } -0.10548 {'sadness' } 0.095211 {'color' } -0.10325 {'heat' } 0.09252 {'object' } -0.10141 {'painrelated'} 0.090739 {'target' } -0.10125 {'unpleasant' } 0.08731 {'attention'} -0.10062 {'sensation' } 0.08518 {'objects' }
Cognitive > PV :: Pattern Phil
[obj, names] = load_image_set('pain_cog_emo');
Loaded images: /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
 
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.0051 -0.7830 0.4362 0.0000 Cog Wholebrain 0.0083 2.3471 0.0217 1.0000 * Emo Wholebrain -0.0026 -0.4048 0.6868 0.0000
axis image
 
subplot(1, 3, 2)
 
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ ________ ________ _________ {'Pain Wholebrain'} -0.0050829 0.0064356 -0.78981 0.43227 -0.093081 {'Cog Wholebrain' } 0.0082967 0.0035346 2.3473 0.021703 0.27663 {'Emo Wholebrain' } -0.0026199 0.0064992 -0.40311 0.68808 -0.047507
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [19.0004 20.0004 21.0004]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
 
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
 
clear csim
for i = 1:3
 
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
 
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
 
subplot(1, 3, 3)
 
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ ________ ________ _________ {'Pain Wholebrain'} -0.0050371 0.0063296 -0.7958 0.4288 -0.093786 {'Cog Wholebrain' } 0.0078987 0.0034451 2.2927 0.024829 0.2702 {'Emo Wholebrain' } -0.002451 0.0063485 -0.38607 0.7006 -0.045499
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [22.0004 23.0004 24.0004]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
drawnow, snapnow;
 

Manipulation check Rating

motor contrast

motor only :: load dataset

clear all;
close all;
 
contrast_of_interest = 'motor'
contrast_of_interest = 'motor'
 
 
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
contrast_name = {
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'motor',... %motor
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
 
};
 
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
spm('Defaults','fMRI')
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead. loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 28753056 bytes Loading image number: 72 Elapsed time is 2.347508 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 6888883 Bit rate: 22.72 bits

motor only :: check data coverage

m = mean(con_data_obj);
% m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans'); % display
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:53:20 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions

motor only :: Plot diagnostics, before l2norm

drawnow; snapnow;
 
[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);
______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 3 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 40.28% Expected 3.60 outside 95% ellipsoid, found 8 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 8 images Cases 8 13 16 18 21 34 40 46 Retained 6 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 41.67% Expected 3.60 outside 95% ellipsoid, found 5 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 13 Uncorrected: 5 images Cases 13 38 58 63 67 Mahalanobis (cov and corr, q<0.05 corrected): 1 images Outlier_count Percentage _____________ __________ global_mean 4 5.5556 global_mean_to_variance 3 4.1667 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 1 1.3889 mahal_cov_uncor 8 11.111 mahal_cov_corrected 0 0 mahal_corr_uncor 5 6.9444 mahal_corr_corrected 1 1.3889 Overall_uncorrected 12 16.667 Overall_corrected 2 2.7778
Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:53:47 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions

motor only :: run robfit

set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;

motor only :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
current length is 72
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 2 participants, size is now 70
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0044" "participants that are outliers:... sub-0088"
disp(n);
{'sub-0044'} {'sub-0088'}

motor only:: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

motor only :: ttest

t = ttest(imgs2);
One-sample t-test Calculating t-statistics and p-values
orthviews(t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:53:51 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
fdr_t = threshold(t, .05, 'fdr');
Image 1 FDR q < 0.050 threshold is 0.041520 Image 1 8 contig. clusters, sizes 1 to 82870 Positive effect: 69078 voxels, min p-value: 0.00000000 Negative effect: 13838 voxels, min p-value: 0.00000000
orthviews(fdr_t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:53:52 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
create_figure('montage'); axis off;
montage(fdr_t);
Setting up fmridisplay objects Compressed NIfTI files are not supported. sagittal montage: 2055 voxels displayed, 80861 not displayed on these slices sagittal montage: 2110 voxels displayed, 80806 not displayed on these slices sagittal montage: 1979 voxels displayed, 80937 not displayed on these slices axial montage: 15292 voxels displayed, 67624 not displayed on these slices axial montage: 16716 voxels displayed, 66200 not displayed on these slices
drawnow, snapnow;
 
 

motor only :: Neurosynth similarity

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ ______________ __________ ________________ -0.33957 {'trait' } 0.39796 {'movements' } -0.3256 {'positive' } 0.37099 {'execution' } -0.32456 {'negative' } 0.36466 {'hand' } -0.31252 {'age' } 0.34996 {'motor' } -0.30539 {'personal' } 0.32699 {'finger' } -0.2965 {'depression'} 0.31052 {'visual' } -0.29385 {'person' } 0.30197 {'sensorimotor'} -0.2933 {'emotion' } 0.28518 {'action' } -0.29102 {'disorder' } 0.28434 {'hands' } -0.2854 {'affect' } 0.27919 {'preparation' }
motor only :: Pattern Phil
[obj, names] = load_image_set('pain_cog_emo');
Loaded images: /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
 
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.0082 -1.8276 0.0718 0.0000 Cog Wholebrain 0.0299 11.6792 0.0000 1.0000 *** Emo Wholebrain -0.0190 -4.3232 0.0000 1.0000 ***
axis image
 
subplot(1, 3, 2)
 
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.008244 0.0045121 -1.8271 0.071892 -0.21532 {'Cog Wholebrain' } 0.029943 0.002562 11.687 2.2204e-15 1.3774 {'Emo Wholebrain' } -0.019015 0.0043979 -4.3236 4.9163e-05 -0.50955
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [19.0005 20.0005 21.0005]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
 
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
 
clear csim
for i = 1:3
 
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
 
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
 
subplot(1, 3, 3)
 
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.0041255 0.004078 -1.0117 0.31514 -0.11923 {'Cog Wholebrain' } 0.026864 0.002331 11.524 2.2204e-15 1.3582 {'Emo Wholebrain' } -0.020797 0.0040213 -5.1717 2.0547e-06 -0.60949
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [22.0005 23.0005 24.0005]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
drawnow, snapnow;
% % save html
% pubdir = pwd;
% pubfilename = '6cond_cueeffect_contrast.mlx';
%
% p = struct('useNewFigure', false, 'maxHeight', 800, 'maxWidth', 800, ...
% 'format', 'html', 'outputDir', pubdir, ...
% 'showCode', true, 'stylesheet', which('mxdom2simplehtml_CANlab.xsl'));
% htmlfile = publish(pubfilename, p);